Here are 5 distance metrics in vector search.
But how do you choose the right one?
• L1 (Manhattan): sum of absolute differences, exact kNN only with no HNSW support
• L2 (Euclidean): straight-line distance, the safe default for most models
• Cosine similarity: angle between vectors, magnitude ignored
• Dot product: same ranking as cosine on normalized vectors, less compute
• Max inner product: dot product without the normalization constraint
Most teams default to cosine and move on. That works until your model outputs non-normalized vectors, and suddenly dot product or max inner product is the better fit.
Scoring formulas and config details in the blog.